The adequate method of representing the KCs' output activity is critical because the output of KCs is responsible for learning behavioral responses to different odors [1]}. In their fly-inspired algorithm,
[2]} use a Winner-Take-All (WTA) approach that models inhibitory feedback ensuring that only the highest-firing 5% of KCs are used on the output. Their WTA circuit selects the indices of the top \(k\)  values of KC activities; however, this method is not ideal for the optimization approach that we introduce later. Therefore, we base our algorithm on a computational model of the fly brain from [3]} that generates sparsity from global inhibitory feedback between KCs [4]}, [5]}, [6]}. In their model, KCs produce an intermediate output that is then subject to global inhibition from the average glomerular input to all KCs. The final KC is then generated by thresholding the inhibited output through a ramp function, which is functionally equivalent the Rectified Linear Unit (ReLU) activation function in artificial neural networks [7]}. Thus, the output of the \(j\) th KC, \(\mathcal {KC}_{out_{j}}\) , was described as:
\(\mathcal {KC}_{out_{j}} = \phi \left( \mathcal {KC}_{in_{j}} - C\frac{1}{N_{\mathcal {KC}}}\sum _{j}^{N_{\mathcal {KC}}}\mathcal {KC}_{in_{j}} \right)\) 
